The invention is directed to a system and a method for diagnosis of engine conditions.
Traditionally, the diagnosis of engine conditions utilizes vibration signals on the basis of amplitude limits. The vibration amplitude limits have been derived from general experience and/or on the basis of features from vibration signatures, from the experience deriving from events during the development phase or, respectively, the experience from the certification or testing process.
Costly and time-consuming modifications in mass production of engines usually ensue.
The vibration diagnosis has been implemented by variously qualified specialist teams without a targeted exchange of experience between operators and manufacturers of engines and without systematic acquisition and interpretation of errors, side-effects or, respectively, symptoms and their causes.
In the previously standard vibration diagnosis of engine conditions, there is thus, among other things, the problem that few measuring positions are contrasted to only a limited amount of information for interpretation. There are in fact error catalogs from the development phase; these, however, are usually full of gaps. The influence of a great number of parameters such as, for example, construction standards, tolerances, size and position of unbalanced masses, temperature effects, performance and flight parameters, etc., as well as non-linearities and measuring imprecisions, remain largely unconsidered.
Given this type of vibration diagnosis, dangerous vibration conditions can continue to exist unrecognized during operation. More serious secondary damage due to late recognition can occur and the outlay for maintenance increases since it is usually necessary to dismantle an engine.
The present invention pertains to systems and a methods for diagnosis of engine conditions. The systems and methods are directed to extraction of features or parameters from different information sources and to processing of the features. These features, together with a series connection of two neural networks, provide a dependable diagnosis of engine conditions, particularly error recognition.
In an embodiment of the present invention, a system for diagnosis of engine conditions has:
a means for supplying statistical/probabilistic information about the error quota of individual engine components resulting from an evaluation of a corresponding data bank and/or
a plurality of measurement sensors for acquiring physical information such as, for example, pressures and temperatures in various engine levels and, moreover, parameters from a particle analysis in used oil and in engine exhaust gases as well as parameters from an analysis of the gas path;
a plurality of measurement sensors for acquiring vibration information in the time domain from an engine;
a vibration analysis means for generating vibration information in the frequency domain from the vibration information in the time domain;
a module for feature extraction for processing the physical information and/or the statistical/probabilistic information and the vibration information in the time and frequency domain and for the extraction of a number of features that comprehensively describe the engine condition;
a first neural network to which the features are applied for classification of the features, for identification of relationships and dependencies between features and for corresponding implementation of an information compression and for output of parameters, whereby the first neural network comprises an input layer, one or more intermediate, layers and an output layer of neurons, whereby the input layer comprises more neurons than the intermediate layer(s) and this in turn comprises more neurons than the output layer, and the neurons of a layer are connected via a plurality of connecting elements having variable weighting coefficients;
a first training means for supplying training input signals to the first neural network and for comparison of the output signal output by the first neural network in response thereto to a training input signal and for the modification of variable weighting coefficients of the first neural network by means of application of a predetermined training algorithm corresponding to the differences between the training input signal and the output signal or for realizing a non-monitored training of the first neural network with the assistance of the training input signals by themselves;
a second neural network to which the parameters output by the first neural network are applied for classification of the parameters, for recognition of relationships between the parameters and specific error constellations, for corresponding implementation of an information linkage and for output of a diagnosis signal, whereby the second neural network comprises an input layer, one or more intermediate layers and an output layer of neurons, whereby the input and the output layer comprise fewer neurons than the intermediate layer(s), and the neurons of a layer are connected to the neurons of the layer following thereupon via a plurality of connecting elements having variable weighting coefficients; and
a second training means for supplying training input signals to the second neural network and for comparing the output signal obtained from the second neural network in response thereto to a training input signal and for modifying variable weighting coefficients of the second neural network by means of applying a predetermined training algorithm corresponding to the differences between the training input signal and the output signal.
In an embodiment of the present invention, the module for feature extraction employs physical parameters such as oil consumption given specific engine runs, power reference numbers such as pressure and temperature in specific engine levels, parameters from a particle analysis in used oil and in engine exhaust gases as well as parameters from an analysis of the gas path.
In an embodiment of the present invention, the module for feature extraction employs methods that are standard for speech recognition, and extracts effective values, properties of the envelopes, modulations, absolute values, performance analyses, statistical parameters, distribution functions, wavelet analysis, etc., of the vibration information in the time domain as features.
In an embodiment of the present invention, the vibration analysis means handles the vibration signals in the time domain and determines corresponding vibration information in the frequency domain therefrom.
In an embodiment of the present invention, the module for feature extraction employs an information presentation in the form of what is referred to as a waterfall diagram, handles this information presentation with image processing methods and determines corresponding features therefrom from the vibration information in the frequency domain.
In an embodiment of the present invention, the module for feature extraction also implements geometrical considerations of the overall image or specific image regions; and/or the module for feature extraction also considers what are referred to as xe2x80x9cskylinesxe2x80x9d of the waterfall diagram from the perspective of the frequency or, respectively, of the time/speed access.
In an embodiment of the present invention, the module for feature extraction also numerically acquires the vibration information of the waterfall diagrams; and utilizes methods from matrix and vector calculation or methods for system identification in the frequency domain for acquiring features from the vibration information in the frequency domain and/or utilizes transfer functions as well as a distribution analysis of the numerical data.
In an embodiment of the present invention, the neural networks in combination with fuzzy logic or pure fuzzy logic circuits are provided instead of the first and second neural networks.
In an embodiment of the present invention, a method for diagnosis of engine conditions has the steps:
supplying statistical/probabilistic information about the error quota of individual engine components resulting from an evaluation of a corresponding data bank, and/or
acquiring physical information such as, for example, pressures and temperatures in various engine levels with a plurality of measurement sensors, as well as parameters from a particle analysis in used oil and in engine exhaust gases as well as parameters from an analysis of the gas path, and/or
acquiring vibration information in the time domain from an engine with a plurality of measurement sensors;
generating vibration information in the frequency domain from the vibration information in the time domain with a vibration analysis means;
processing the physical information and/or the statistical/probabilistic information and/or the vibration information in the time and frequency domain and extracting a number of features that comprehensively describe the engine condition with a module for feature extraction;
classification of the features, identification of relationships and dependencies between features and corresponding implementation of an information compression and output of parameters by a first neural network to which the features are applied, whereby the first neural network comprises an input layer, one or more intermediate layers and an output layer, whereby the input layer comprises more neurons than the intermediate layer(s) and this in turn comprises more neurons than the output layer, and the neurons of a layer are connected via a plurality of connecting elements having variable weighting coefficients;
supplying training input signals to the first neural network and comparing the output signal output in response thereto by the first neural network to a training input signal and modifying variable weighting coefficients of the first neural network by means of application of a predetermined training algorithm corresponding to the differences between the training input signal and the output signal or for realizing a non-monitored training of the first neural network with the assistance of a training input signals by themselves with a first training means;
classification of the parameters, recognition of relationships between the parameters and specific error constellations, corresponding implementation of an information linkage and output of a diagnosis signal by means of a second neural network to which the parameters output by the first neural network are applied, whereby the second neural network comprises an input layer, one or more intermediate layers and an output layer of neurons, whereby the input and the output layer comprise fewer neurons than the intermediate layer(s), and the neurons of a layer are connected to the neurons of the layer following thereupon via a plurality of connecting elements having variable weighting coefficients; and
supplying training input signals to the second neural network and comparing the output signal obtained in response thereto from the second neural network to a training input signal, and modifying; variable weighting coefficients of the second neural network by means of application of a predetermined training algorithm corresponding to the differences between the training input signal and the output signal with a second training means.
In an embodiment of the present invention, the acquired, physical parameters are an all consumption at specific engine runs, power reference numbers such as pressure and temperature in specific engine levels, parameters from a particle analysis in used oil and in engine exhaust gases as well as parameters from an analysis of the gas path.
In an embodiment of the present invention, specific engine components or parts are, for example, classified as especially susceptible in the feature extraction on the basis of the statistical/probabilistic information and these information are output in the form of features.
In an embodiment of the present invention, methods as standard for speech recognition are employed in the processing of the information and extraction of features, and effective values, properties of the envelopes, modulations, absolute values, power analyses, statistical parameters, distribution functions, wavelet analysis, etc., of the vibration information in the time domain are extracted as features.
In an embodiment of the present invention, the vibration information in the time domain is processed with a vibration analysis means and corresponding vibration information in the frequency domain are determined therefrom.
In an embodiment of the present invention, an information presentation in the form of what is referred to as a waterfall diagram is employed when processing vibration information in the frequency domain, this information presentation being handled with image processing methods and corresponding features from the vibration information in the frequency domain being determined therefrom.
In an embodiment of the present invention, geometrical considerations of the overall image or of specific image regions are implemented as well when processing vibration information in the frequency domain, and/or what are referred to as xe2x80x9cskylinesxe2x80x9d of the waterfall diagram are also extracted when processing the vibration information in the frequency domain viewed from the perspective of the frequency or, respectively, of the time/speed access and corresponding features are extracted therefrom.
In an embodiment of the present invention, the information of the waterfall diagrams are also numerically acquired when processing vibration information in the frequency domain, and methods from matrix and vector calculation or methods for system identification in the frequency domain are utilized for acquiring vibration information in the frequency domain and/or transfer functions as well as a distribution analysis of the numerical data are utilized.
In an embodiment of the present invention, the classification, identification, information compression and output of parameters and the classification, recognition of relationships, information linkage and output of a diagnosis signal is implemented by neural networks in combination with fuzzy logic or by pure fuzzy logic circuits instead of by the first and second neural networks.
An object of the present invention is therefore to create a system and a method for diagnosis of engine conditions, whereby the dependability is enhanced on the basis of a recognition of dangerous vibration conditions, more serious secondary damage is avoided due to an early error recognition, the outlay for maintenance is reduced by targeted elimination of the causes of vibration and maintenance ensues according to the current condition of the engine (i. e., xe2x80x9con-conditionxe2x80x9d).